Nowadays, Python has almost become an ubiquitous programming language. People from various backgrounds utilize Python to create all kinds tools to facilitate their jobs or simply for fun. Although Python is itself a simple language, organizing Python code or creating a Python-based package could be a bit overwhelming for newcomers including myself (what's a license, why do we use conda, what is PyPI, what are all those weird configuration files, etc.).

Being an absolute novice myself, I myself am still exploring this vast and fast-evolving field. However, during this process, I find it extremely helpful to jot down what I learned so far for future reference. This can serve as a knowledge base. So, here we are 😄.

Before I get started, I would like to give credit to Kevin Wang. This is his package that inspired me to do this 👏.

(BTW, if you are wondering how I added Emojis, here is a great website for your reference).

Of course, there is no way for me alone to cover all difference user cases. Coming from a background of Statistics, in this repo, I will mainly focus on creating a Python-based package for scientific computing purposes (kind like numpy but a really shabby version 😳).

Currently, I am planning to walk through every single step from creating a Github repo to publishing the package (on PyPI and/or Conda). The following content are included in the online document

• Github
• Conda
• Directory structure
• Unit tests
• Read The Docs
• PyPI/Conda
• Miscellaneous reference
  • Markdown
  • reStructuredText
  • Emoji
  • License

Of course, as I am learning, the content of the repo will be constantly updated.